mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
171 lines
4.4 KiB
YAML
171 lines
4.4 KiB
YAML
# This file runs RLlib algorithm learning tests for select algorithms on TF.
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# It is suggested to run these on a single g3.16xlarge or p3.16xl node
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# in a DLAMI / tensorflow_p36 env.
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# Note: RL runs are inherently high variance, so you'll have to check to
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# see if the rewards reached seem reasonably in line with previous results.
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# You can find the reference results here:
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# https://github.com/ray-project/ray/tree/master/doc/dev/release_logs
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a2c-tf-atari:
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env: BreakoutNoFrameskip-v4
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run: A2C
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num_samples: 2
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stop:
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time_total_s: 3600
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config:
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framework: tf
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rollout_fragment_length: 20
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clip_rewards: True
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num_workers: 5
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num_envs_per_worker: 5
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num_gpus: 1
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lr_schedule: [
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[0, 0.0007],
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[20000000, 0.000000000001],
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]
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apex-dqn-tf-atari:
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env: BreakoutNoFrameskip-v4
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run: APEX
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num_samples: 2
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stop:
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time_total_s: 3600
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config:
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framework: tf
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double_q: false
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dueling: false
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num_atoms: 1
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noisy: false
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n_step: 3
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lr: .0001
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adam_epsilon: .00015
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hiddens: [512]
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buffer_size: 1000000
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exploration_config:
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epsilon_timesteps: 200000
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final_epsilon: 0.01
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prioritized_replay_alpha: 0.5
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final_prioritized_replay_beta: 1.0
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prioritized_replay_beta_annealing_timesteps: 2000000
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num_gpus: 1
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num_workers: 8
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num_envs_per_worker: 8
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rollout_fragment_length: 20
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train_batch_size: 512
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target_network_update_freq: 50000
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timesteps_per_iteration: 25000
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dqn-tf-atari:
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env: BreakoutNoFrameskip-v4
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run: DQN
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num_samples: 2
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stop:
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time_total_s: 3600
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config:
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framework: tf
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double_q: false
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dueling: false
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num_atoms: 1
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noisy: false
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prioritized_replay: false
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n_step: 1
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target_network_update_freq: 8000
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lr: .0000625
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adam_epsilon: .00015
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hiddens: [512]
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learning_starts: 20000
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buffer_size: 1000000
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rollout_fragment_length: 4
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train_batch_size: 32
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exploration_config:
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epsilon_timesteps: 200000
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final_epsilon: 0.01
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prioritized_replay_alpha: 0.5
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final_prioritized_replay_beta: 1.0
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prioritized_replay_beta_annealing_timesteps: 2000000
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num_gpus: 0.5
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timesteps_per_iteration: 10000
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impala-tf-atari:
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env: BreakoutNoFrameskip-v4
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run: IMPALA
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num_samples: 2
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stop:
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time_total_s: 3600
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config:
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framework: tf
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rollout_fragment_length: 50
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train_batch_size: 500
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num_workers: 10
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num_envs_per_worker: 5
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clip_rewards: True
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lr_schedule: [
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[0, 0.0005],
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[20000000, 0.000000000001],
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]
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num_gpus: 1
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ppo-tf-atari:
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env: BreakoutNoFrameskip-v4
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run: PPO
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num_samples: 2
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stop:
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time_total_s: 3600
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config:
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framework: tf
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lambda: 0.95
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kl_coeff: 0.5
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clip_rewards: True
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clip_param: 0.1
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vf_clip_param: 10.0
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entropy_coeff: 0.01
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train_batch_size: 5000
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rollout_fragment_length: 100
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sgd_minibatch_size: 500
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num_sgd_iter: 10
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num_workers: 10
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num_envs_per_worker: 5
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batch_mode: truncate_episodes
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observation_filter: NoFilter
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vf_share_layers: true
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num_gpus: 1
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# Expect roughly 1000 reward after 1h on 1GPU
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sac-tf-halfcheetah-pybullet:
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env: HalfCheetahBulletEnv-v0
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run: SAC
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num_samples: 2
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stop:
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time_total_s: 3600
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config:
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framework: tf
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horizon: 1000
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soft_horizon: false
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Q_model:
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fcnet_activation: relu
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fcnet_hiddens: [256, 256]
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policy_model:
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fcnet_activation: relu
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fcnet_hiddens: [256, 256]
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tau: 0.005
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target_entropy: auto
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no_done_at_end: true
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n_step: 1
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rollout_fragment_length: 1
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prioritized_replay: true
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train_batch_size: 256
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target_network_update_freq: 1
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timesteps_per_iteration: 1000
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learning_starts: 10000
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optimization:
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actor_learning_rate: 0.0003
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critic_learning_rate: 0.0003
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entropy_learning_rate: 0.0003
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num_workers: 0
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num_gpus: 1
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clip_actions: false
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normalize_actions: true
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evaluation_interval: 1
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metrics_smoothing_episodes: 5
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